A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

Abstract

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

“…To enhance the efficiency, a novel zero-shot learning technique is presented by combining features and learning discriminant latent attributes that could resolve the imbalance challenge of constitution classifications. Kamarudin et al [21] proposed a 2 phase tongue multicolor classification depending upon SVM that is decreased by this presented k mean clustering detectors and red colour range to diagnose accurate tongue colour. Initially, k-means clustering is utilized for clustering a tongue image to 4 clusters of deep red region, image background (black), transitional region, and red or light red region.…”

Advances in automated tongue diagnosis techniques

“…To nullify inconsistent or varying lighting conditions, another current trend is hyperspectral imaging 33, 35. Another SVM-based work is recently reported to attain 94% accuracy to classify four tongue-related colors, that is, red, light red, and deep red 46 . This work encompassed classification in two stages.…”

“… 33 , 35 Another SVM-based work is recently reported to attain 94% accuracy to classify four tongue-related colors, that is, red, light red, and deep red. 46 This work encompassed classification in two stages. The first stage involved the unsupervised machine learning, k-means.…”

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

“…Arti cial Neural Network [13], Support Vector Machine [14], K Nearest Neighbor [15], and other machine learning methods have helped to achieve the digitalization of TCM tongue & pulse diagnosis and the establishment of the corresponding disease diagnosis model [16; 17]. The diagnostic relationship between tongue & pulse and health state can be better established through accurate detection, identi cation, and multi-dimensional quantitative analysis of tongue & pulse data to save medical resources and improve diagnosis e ciency and treatment e cacy [18][19][20]. Data-driven researches on fatigue diagnosis technology using tongue & pulse data have been increasing day by day.…”